Sickbay Sessions | Episode 3: Pragmatic Improvements in Clinical Care: Using a Data-Driven Approach to Feasibly Improve Outcomes
June 12th | 11:30PM CST
Presented by:
Amber Glauser, RN MSN-I (Vice President Solutions) & Erik Pemberton RN (Solutions Architect)
Healthcare executives are under pressure to do more with less—less staff, less time, and tighter margins. But scalable, tech-driven clinical innovation is possible.
Join us for the third installment of Sickbay Sessions, where we’ll explore how data aggregation and smart platforms like Sickbay are helping hospitals rethink care delivery by improving outcomes while protecting operational efficiency.
What You’ll Learn:
- Why traditional EHR-based systems can’t support scalable, clinical decision-making
- How Sickbay enables continuous monitoring and actionable analytics
- Success stories from ICUs and virtual care teams across the U.S.
- What comes next for innovation and outcomes
Designed for healthcare executives and clinical leaders looking to align innovation with outcomes.
Podcast Transcription:
Jennifer: Thank you all for joining us today. I’m Jennifer Lazars, the director of marketing for Medical Informatics Corp. We are thrilled to bring you the third episode of Sickbay Sessions, where we’re exploring how cutting-edge technology is revolutionizing bedside and remote patient monitoring, optimizing clinical workflows, and closing the gap in data in healthcare. Each session features industry experts and innovators to discuss the latest industry advancements and solutions throughout the session. Today, if you have a question or comment, please feel free to use the tools that are at the bottom of the screen, we will be interacting with you during the session, and you can find that it’s definitely labeled Q and A, so if you have any questions, you can chat us and I will help you. Here at Medical Informatics Corp, or as we call it, MIC, we are proud to deliver next generation technologies to unify patient monitoring workflows for healthcare systems. Our focus is on every patient everywhere, monitored by people who care, powered by actionable data. And with that, I’m going to hand things off to Amber Glauser, Vice President of Solutions, and Erik Pemberton, a solutions architect from here at MIC, they are clinical and technical solutions experts who are here to discuss how technologies are actively transforming healthcare.
Amber: Yeah, thank you, Jennifer and Hi everyone. Thank you all so much for joining us today. I’m Amber Glauser, and I’m very excited to also be here with my colleague, Eric Pemberton. And as nurses, we both worked across the front line at systems level roles, we also know firsthand how the immense pressures that are facing hospitals today. Eric, do you mind sharing a little bit about some of the challenges that you’ve seen firsthand over the years that are continuously evolving?
Erik: Absolutely, you know staffing shortages being a big one, combined with rising patient acuity and created what we all know, and that is an unsustainable care model today. Then add in declining reimbursement rates. Hospital leaders, like yourselves on this call, are under constant pressure to do more with less. That’s really the ecosystem in which we’re operating.
Amber: Yeah, I’m glad you brought up the staffing shortage piece. It’s one of the things I find myself frustrated with, not just that we have it, but if you all recall, back in around 2010 or so, the Institute of Medicine actually put out a report for the vision of nursing for 2020, and back then, they were saying, Guys, we’re headed in a bad direction. So, we really needed to start thinking about technology solutions back then, so that we can be prepared for what we’re all being strained with today. But we’re really here just to share some pragmatic and scalable strategies that can help to solve some of these problems and use a data driven approach and to help to navigate all of these challenges, while simultaneously helping to improve patient outcomes without adding other cost burdens, which we know is always a major consideration when we’re thinking about the future of how we’re actually delivering health care.
Erik: Yeah, you know, let’s talk pragmatically and honestly about where the current systems fall short. Traditional EMR-based solutions are often kind of static, retrospective point in time, and a lot of time are siloed to just that information. Their Foundation was really a way to document things right, to be able to bill the insurance companies and drive revenue and reimbursement for hospitals and organizations. I think we can all agree that no one has ever said, you know, the electronic EMR has made my job easier. That doesn’t happen. They were never designed to support timely, dynamic decision-making at the bedside or even across service lines. I’m an elder nurse, and my career actually started before the EMR and I remember the days as an ICU nurse of the big tri quad fold paper flow sheets where we wrote down all of our vital signs by hand. But what we also did is drew that little trend line up and down the paper. That’s where it really began for me. And those things when we went to EMRs are some of the things that that went away,
Amber: I would say, sometimes went away as a part of that. I’m glad you brought up that trended line, because I personally also started on the paper documentation too, and I am very familiar with seeing those gorgeous railroad track blood pressures that everyone magically had. So, we all had that idea and that vision too at that point, that those types of things were going to go away, because now we’re in a digital system for our vital signs. I hitched my wagon onto the EHR train, I will say. And I was like, this is our future. We’re going to revolutionize healthcare by being digital, which many wonderful things did evolve from that. But with going deep in the EHR world, I also realized, actually, we were still doing a lot of the same practices, but now in a digital format. So for example, even on that beautiful, gorgeous blood pressure that was always perfectly 120 over 70, that actually didn’t change, because the nurses still had the ability to override some of that objective data as well, to commit those beautiful numbers in the legal medical record, understandably so. But that really is what was still happening, that we were kind of pre-cleansing some of that data, which made these unreliable data sets as our foundation. So, when I started out though, in the EHR world, as I mentioned saying, this is our future. It’s so incredible. We’re going to get to predictive analytics from this, I was really bought into that vision around once we have these large data sets in our EHR, we’re going to make these incredible predictive and precision models. We’re going to put those into the hands of clinicians, and they’re going to be perfect. We haven’t seen that happen yet. Unfortunately, we’ve seen attempts that have gotten better than where we used to be, but they’re still not really to the accuracy or the specificity of where they should be, and that’s really, again, because of the way that those systems were designed, they really weren’t designed for the integration or the scalability, and so it created these types of data bottlenecks where you can’t also extract those insights fast enough, and you can’t connect the right points, and ultimately, you just can’t respond effectively to the complexity that we have in those modern clinical environments that we all know is demanded right now with our rising acuity of patient care too. So Eric, I want to kind of transition though and talk a little bit about how what we do now is different from what we were doing in our EHR, it days and at MIC, I can share I personally was drawn to this company because of the true potential of finally realizing that future of predictive and precision analytics. So, just want to share with everyone Sickbay. Earlier, Jennifer had shared with you all the name of our company, Medical Informatics Corporation, but our software platform is called Sickbay, and Eric and I, both, as nurses, really have been very excited about all of the new evolution that is happening with having this type of technology in place. So just to help get anyone up to speed that hasn’t heard of this product, it really offers multiple different modules around the physiological monitoring world, so giving the ability for clinicians to have additional access to patient monitoring information, multi patient team monitoring, as well as retrospective access to all of the full native resolution wave form of physiological monitors, regardless of the monitoring vendor. Now, of course, having that data in one place is great, but just as I mentioned the challenges of the EHR not being able to create those really reliable, predictive or precision analytics. The real power of Sickbay becomes that full data set of the actual objective data gathered from all of those different sources into Sickbay, which allows you then to have tools to develop and deploy analytics to power all of your quality initiatives and to create custom data insights.
Erik: Yeah, I definitely agree with that. I was also drawn to MIC and the Sickbay platform because of the possibilities that are there for us. I think one of the things that makes Sickbay really stand out for me is how it enables the continuous monitoring of multiple systems, not just a physiologic monitor, but the vents, mirrors, ECMO, all of those devices, and then transforming that data into an actionable insight, not just the collecting of data, but the so what that comes with it? It’s not just built for one department, either. It’s really an enterprise-wide aggregation platform. It enables you to use it for virtual care and long-term operational transformation and efficiency within your organization. It really is a long-term journey that you can go on, and this platform is really designed to help you get there.
Amber: Well, I’m glad you just mentioned there the efficiency part, again, US disclosing our elderly nurse status here, but we all have that experience too, of being in these very critical environments. And unfortunately, the way that the systems were designed, having to go from device to device to device, to gather information and then try to synthesize all of that data, which became a really manual process for us at the bedside. So honestly, I’m very jealous I didn’t have Sickbay at the bedside, because I could have saved a lot of time in trying to get the data, but then also being able to quickly synthesize it in a way that we have never seen before now, because technology has enabled it.
Erik: Yeah, you know, let’s talk about the ways that we’ve seen kind of these incredible pan impacts across the country, with the places that that Sickbay has been deployed. For example, like in the ICU, it improved team efficiency for it because It centralizes that patient. Data from all of those previously siloed devices. So it’s not a matter of, look here, look here, look here, and here’s the full clinical picture. It’s more of a take a look at that, that old adage of a picture is worth 1000 words, but it’s really a dynamic, moving picture, if you will, that streaming data that you get with all of those devices included in and I
Amber: I will say, though, real quick, there, Eric, this is a good one, because I can recall many night shifts calling my cardiothoracic surgeon. Um, he I’m waking him up in a fog and I’m going, like, here’s what I think I’m seeing from all of these different devices. And he’s like, you can’t just think you’re seeing it. I need to know what being so I, you know, we would talk a lot about doing the charades where I’m trying to act out, you know what these waveform pressures are looking like, versus now, this new technology would have allowed me to just send him a text where he can actually, literally see what I’m seeing right there at the bedside, even though he’s just woken up from a slumber.
Erik: Yeah. Yeah. Story Time with doctors was always a fearful thing, especially when you’re a new grad or a brand new nurse, and you’re waking up this, this really experienced physician, you don’t want to screw it up there, and then you might be a little timid to even call them, which means you’re really delaying care and having that, that capability of of going through and giving it to him. I did want to mention, though, we talk about texting, securely texting with a phi free link for that information. So, whether you’re doing secure messaging apps or people are doing it on the sly on their phone, there’s still some protections baked into that process. And then we look at it again. What about virtual and bedside multidisciplinary teams being able to collaborate, so not just the nurse talking to the doctor, or nurse to nurse, or that that consult. I’m the intensivist, and I want to get the input from the cardiologist on something I’ve I’m seeing. It’s a general ICU, or maybe it’s a neuro patient, but they’re having ST elevations or something like that. The ability to do that multidisciplinary consult, but without having to physically, geographically come to the ICU or the care environment to see that information. And then what about other areas besides not just our nursing and our providers, but what about Respiratory Therapy? Vents these days are really, really advanced on what they can do, and people are a little timid to make changes to it, so they always want to involve the respiratory therapist, but they’re running around the building ping ponging from unit to unit because there’s not a lot of them. And so the ability for respiratory to be able to do that, that assistive information, and give it to them and say, hey, yeah, go ahead and bump the PEEP up by a couple. I’ll be down in a little bit that sort of thing. So, it really does change that, that care dynamic and the clinical decision making that we’re able to do, and do it at a much faster, much safer by leveraging these technologies.
Amber: Yeah, I do have questions for the architects that design hospitals. I’ve not seen one yet that puts RT in a conveniently located position.
Erik: Off in the bowels of the hospital somewhere.
Amber: Definitely in the far hallway somewhere. But this is a great example, though. Because of that, even with that challenge, they would also love to have additional surveillance over those ventilators so they can also be more proactive. So that’s something that’s been really exciting to see, the rise of virtual programs. And the reason why I’m really passionate about that is because I want to just take a second too. I know you shared about your paper documentation, flow sheets. I have my historical story about virtual care as well. So, 20 years ago, I was practicing in a critical access hospital in the middle of nowhere, Kansas. We had, at that time, a very advanced virtual ICU program. So what that meant was, while I had my patients in the ICU, if I got stuck in a bind and I needed to have an intensivist provide me a consult, you all might be able to imagine that recruiting on staff 24/7 intensivists in nowhere Kansas, is not an easy challenge, so they put up this technology solution to enable the coverage in those rural environments, which makes a ton of sense. So, I had this cool red button on the wall that I could slap once I’m in trouble. Remember the keyword there? I had to already be in trouble and know that I’m in trouble, to let the intensivist know, and then they could come up on a camera and say, “Oh, it looks like your ET Tube moved.” These are the types of things that I dealt with daily, but just important to know that those were intensivists that those were three hours away from me, and we were doing this 20 years ago. So, question for Erik, why do you think that it’s been so slow to evolve in the innovation space as an industry, why is it 20 years ago, places are still thinking about that as the future of virtual care?
Erik: Yeah, that’s good question. Um, I think you start with the technical limitations that we’ve talked about, where you’ve got those physiologic monitors, and then you have. Separate ventilator data, and that separate all of the other devices in there and trying to bring it all together. So you have not just the technical limitations that have slowed that advancement, but we also have the reimbursement models, because ultimately, you know, we it’s a business, whether we like to say that or not, and a lot of the reimbursement models that have existed don’t really account for services that are delivered by telehealth. How do you pay that intensivist overnight to cover 12 different hospitals? How does that reimbursement? Do you charge it by the button push? Do you charge it by the hour? What does that really look like? And it really wasn’t spelled out very well. Now we saw a lot of these policies change recently due to covid, it drove a lot of people to look at virtual care differently with it, and just now, hospitals are really getting into a prime position to start planning all of their policy and practice changes that come with deploying a truly strategic, virtual acute care type of support model within their organization, and be able to cover it financially with some sort of reimbursement that comes with it. I think that really has slowed, slow deployment, but we’re on the cusp.
Amber: Yeah, I think that, I mean, really helps to also highlight the reason why we as clinicians also need to be at the forefront advocating for policy change to accommodate for these, you know, new developing care delivery models that we need to make sure that we’re accounting for the not just reimbursement side of it, but what other types of moving systems around it that we would need to consider, you know, not just the legal policy and reimbursement policy, but even at the hospital level, what policies does that actually mean? Whereas the reach of those virtual care providers, what is the full, comprehensive rule their liability, these are all things that are just now because of the change in policy that’s allowed for some of that policy reform to allow for reimbursement, hospitals are just now getting their heads wrapped around all of the other moving parts for virtual care, not just the technology, because, as we all know, it’s always people process and technology. It is just really frustrating though, that we just keep seeing these barriers evolving in healthcare. And so we have to really think critically about what we’re going to be doing and how this is going to impact not just our staff, but also our patients. So, I did mention, though earlier that I was excited to join MIC for that potential for truly effective predictive analytics models and detecting patient deterioration earlier. Do you mind sharing some of the real-world examples you’ve been working with clients on?
Erik: Yeah, yeah. I think the one as an old ICU nurse that we call that our age numerous times here, uh, elder nurse status, um, I think the one that really excites me a lot is Code Blue predictor type models. There are a few of them out in the environment right now. And the one that we’re actively working with one of the clients is looking at all comers to the ICU, in roughly 90% of the cases, they’re predicting a code blue deterioration 30 to 35 minutes early. And I love the data science guys, because they’re like, well, it’s only 90% and it’s only 35 minutes. And as a nurse, I’m like, I take 50%, I’d be okay being wrong half the time, and give me five-minute head start. Okay, minutes, we have time to get back over there. So, 35 minutes and 90% accuracy is huge, but they’re always evolving, and a lot of that has to do with the improvements in that data collection and the data aggregation, more data points being pulled in to make those models more accurate, more precise in what they’re looking at, less inaccurate in false positives that they have. Another one that I think is really, really interesting is the fact that we can get down to very detailed patient populations and demographics. So there’s a pediatric facility that we work with, and they have developed one for HLHS, hypoplastic left heart syndrome. These are your single ventricle kiddos that you have there, and so trying to develop a deterioration index for them specifically, your traditional pediatric deterioration early warning score is not going to apply. Their physiology is just so different. So they’ve actually developed one just specifically for that demographic, for that population, and so they can apply that precision analytic for just those individual patients who probably you really do need to get a head start and get ahead of their deterioration, because they don’t have a whole lot of capacity to rebound and be resilient to that. And I would say probably the third one that comes to mind is in the neuro ICU, working with those traumatic brain injury. Uh type patients that they’re looking at the tools for a condition called PSH, proximal sympathetic hyperactivity. Uh, storming is what some people refer to it as, and they’re doing a lot of really good work about it. The trick is, the whole idea of PSH is that all of your vital signs go up at the same time, so increased heart rate, increased blood pressure, increased temperature, all of these different things. So, it kind of looks a little bit like some of the conditions you get from a stroke. It looks a lot like you might be having a seizure. So, how do you do the differentials on this? And what we’re really looking at is with the analytics and the capability that we can predict all of those increases over a x period of time, and raise that, that sort of flag to say, hey, you might want to come do some of the subjective and objective tools that you have to really differential. Is this a seizure? Is it some sort of noxious stimuli, or is this? PSH, and so there’s a lot of work being done around the tools to get the clinician at the bedside earlier to put their expertise to work with those differentials. So when we talk about AI, people get a little nervous that we’re going to have technology making clinical decisions for us. That’s not necessarily where we’re going. I think of it as a specialized medical tool, a really fancy blood pressure cuff, if you will. That simply gives you another determinant, another measure, that you can then raise that virtual flag and say, Hey, clinician, come do what you do best, and that is really make those differential diagnoses. So I think those are the ones that excite me.
Amber: Yeah. I mean, that’s really cool. And what you just hit on there too, is a shift in the overall way that we approached I mentioned earlier my camera example, where I would need to do my subjective assessment first, then say, “Hey, doctor, take a look.” Do you mind elaborating a little bit more about how we’re flipping that on its head by these types of analytics being introduced at the bedside with the clinicians?
Erik: Yeah, yeah. As you kind of mentioned, historically, we’ve had a subjective first approach. It’s what the clinician is seeing right now, in the moment that they’re looking at, it’s really difficult to know, well, has that been going on for a certain period of time? These new advances and these new analytics really give the care team a more proactive versus reactive approach, meaning we lead with the objective data. First, we’ve looked at a large cohort of multiple variables. We’ve looked at it, we potentially ruled out some of the artifact, the junk, if you will, in it, and then we give you that indicator. Then we supplement that with coming to the bedside, looking at it and validating with the subjective data that we’re seeing and confirming Did, did those objective measures tell us actually what we’re seeing, which is really, as you said, flipping it on his head, it’s a completely different way to look at the way we’ve practiced compared to previous years, previous decades, even.
Amber: Yeah, as a data lover, I can’t wait for all this evidence-based medicine to continue to evolve. You and I, earlier this morning, we were just chatting about how even the new devices are introducing new data into the mix. So, we were sharing with each other. I didn’t even have those digital pupilometers at the bedside, so that’s one example of an evolution of a device that brought a new data point into the mix of these types of assessments, but now with advanced commute compute, you can actually apply those into the hands of clinicians and do that pattern recognition really quick from all those 1000s and 1000s of data points that we’re collecting on these patients at any given moment. So I also do want to share a little bit about one of my passions. I know you kind of hit on that precision analytic being applied to a specific population, which is great that you were kind of talking about the combination of predictive, we’re predicting an event, and we’re being very precise about which populations we’re applying it to. So your PSH is meant to be in a neuro ICU. Your HLHS predictor is meant to be in a NICU. I have one that I am very passionate about, that is a precision analytic that is, I believe, going to really revolutionize a lot of what we do in healthcare as well. I actually came to MIC because in my past life, I had helped to develop a quality measure, and it was really to help understand the role of interoperative hypotension and its impact on the development of acute kidney injuries and acute myocardial infarctions. And what I learned was that we didn’t have an answer. We were still just trying to figure out, Is this the cause of our problem? But what do we do once we know it is a problem, and there wasn’t anything at that point? So, I learned about the new evolution in the scientific community around the potential of using and applying cerebral auto-regulation indices. So I knew when I heard that this is the new evolution, where we’re actually taking that biofeedback for auto-regulation. Monitoring, we’re on the precipice of something massive as a scientific breakthrough. So, this concept means we’re finally going to start really doing precision medicine. So, my railroad tracks I mentioned to you earlier aren’t always perfect for every patient and every condition in every scenario, and so now looking at this, we can actually look at the individual patient and start to have an idea of if they actually are reaching optimal cerebral perfusion with the level of blood pressure that we’re keeping them at. So really looking at that individual, that holistic patient, in front of me, and looking at their physiological responses to give those data driven insights to those clinicians for optimal decision making. So, I’m so excited that we’re finally here. We’re really getting into actually treating individuals, versus our old textbook way, everyone needs a mean arterial pressure of 65, no matter what’s going on with you. And we’re finally saying, let’s treat the individual.
Erik: Yeah, yeah. I think that’s really a key point, because normal for a 25-year-old athlete and normal for someone who has lived eating cheeseburgers for the last 40 years, that you know coronary artery disease and hypertension. What’s normal for them is not normal for everybody, and so we really need to start focusing on the individual, and not just bell-shaped curves of people. Now I do want to point out that a lot of what we’ve talked about here is data points and data-driven and research and all of that. Just keep in mind that while all of these tools are really helpful for research, having those 1000s of data points that we’re talking about are great for research, but these tools are really built for the inaction at the bedside, clinical use. And so the clinical use drives the question, that drives the research, that drives the improvement. So, these are not just research tools that we’re really talking about. These are at the bed, real-world type tools. Now the research is important, but I think it’s a combination of the two working hand in hand, and the researchers and the providers and clinicians who many of them are scientists themselves and are doing research and publishing on right along with it, the two really go hand in hand, and it’s a good synergy for that effect.
Amber: Yeah, well, you make a good point there too, because I had always been educated in the world of evidence-based practice that we should be incorporating the latest scientific evidence into the hands of clinicians. What I think has been interesting to see, though, is kind of a cognitive dissonance, where, because a lot of times, technology only allowed evolution of using analytics and data, there were barriers and boundaries, and being able to have open systems that allowed you to deploy these types of analytics. So what happened is a lot of folks said research is just a thing for writing an academic paper, right? And instead of realizing we do research every day in our quality departments, we’re constantly saying, let’s evaluate an outcome that we’re seeing we don’t like that outcome. Now let’s work backwards and redesign the system to improve the outcome. That’s exactly a real-world example of research. Then, of course, there is the academia side as well, that once they have these profound findings and they’ve done these large, multi-institutional studies, we should be getting that right back into the hands of bedside to provide the latest and greatest evidence-based care at the point of care. So, I think the other important part of not just partnering with researchers and partnering with quality departments for research. Those are obviously very important, but we also have the need of working with infrastructure. This is the boring side. I know everyone who doesn’t have our IT backed knowledge. Trust me, I tell everyone about my transition over to it. I was very ignorant. I thought every application was just that. Back then, we had the CDs, the discs, and so I thought every application just came as TurboTax that you would buy at Walmart. I did not realize actually how software development worked then, but important things just to know that, as we’re thinking about these types of clinical deployments that we were discussing earlier, the really unique thing with Sickbay is you don’t have to rip and replace all of your existing infrastructure. You don’t have to take out all of your bedside monitors. You don’t have to take out your EHR. This is really complimentary, and it layers on top of all that data, which unlocks all of the value of the types of data and information that you’re already generating and now allowing it to work for you.
Erik: Right, right? I think you bring up, yeah, some valid points. Were you guys, I’m sorry, you’re gonna say something else.
Amber: No, no, that’s great. I think, though, just as we are looking forward and we start to think though about the integration of AI into that workflow, I don’t believe that this is going to just still be a nice-to-have. We’re seeing the AI race happening right now, and it’s just becoming a necessity. It can help us in so many different ways, from augmenting our staffing, improving our triage, driving those early interventions.
Erik: Yeah, you know, I think this is Erik’s prediction for the future. I think in five to 10 years, these are going to be part of the standard of care, just like we’re used to, what whatever the vital signs are, and those are considered standards, these AI-enabled tools, these clinical workflows, these precision analytics, are really going to be part of everyday practice. It’s going to be a must-have, if you will. Imagine if you walked into a hospital today with your son or daughter, your child, and said, Hey, I think they broke their arm, and the hospital said to you, oh, we don’t do radiology diagnostics here, you would immediately get in your car and go somewhere else and say, Yeah, never again there. This radiology is not a nice-to-have. It’s a basic part of the way we deliver healthcare. I think that’s how these tools will be in the near future, maybe five years, maybe 10 years, don’t really know, but I think these will become more than just a nice-to-have as we move forward and learn from them. I think to make that happen, though, we really need to think about building the infrastructure in the buildings and the scalable virtual care that we have. I think that’s really crucial. When I speak to organizations and say, What are your goals for your virtual care program? The answers are very they’re wide, all over the place. To some people, it just means we’ve installed cameras so we can start doing admin and discharge and med reconciliation with our patients, but we’re really not monitoring and surveilling patients. Other facilities are looking at it and say, No, we’re establishing a centralized command center, a surveillance program where we have critical care nurses, intensivist doctors, specialists, providing a second set of eyes for all of our patients, not even just one building, many times because it is remote over multiple facilities, and that ties all the way back to some of our earlier conversations about, how are we going to get paid to do that? But I think in all of the cases, it’s really you have to look at this pragmatically and say, this is an iterative process. You’re not going to boil the ocean in one fell swoop. You really have to pick one item and start that process, and then develop on top of that, and what’s the next thing that we’re going to accomplish? It really is a journey for all organizations. So, when you’re looking at these models, the infrastructure, the tools, where do we want to start it? It’s really finding a point and saying, This is our starting point, and then we’re going to build, and the nice thing about it, I’ll say, Sickbay is somewhat future proof for that. It’s not a static platform. It is modular, it is scalable, and it can grow and evolve with your organization as your quality improvement and research, and your AI road map is developing. We, with Sickbay, can develop right along with you. It’s flexible to meet your needs as you grow and as you change your requirements.
Amber: Yeah, I would say on that note, some of the most transformational CIOs I have worked with also understand the value of at this time, you can no longer simply pick vendors. You need to pick technology partners. And for just exactly what you mentioned, Eric, is that we have this need to really partner deeply, to transform the way that we’re delivering care. And not every hospital is exactly on the same path or the same level of maturity, and what they’re doing. And so it does take a technology partner like us at MIC that can meet you where you are to start, and then continue to grow that partnership and what you all are accomplishing in that digital space to really realize a full digital revolution, which is what we are really advocating for in healthcare. I think we have maybe, you know, gone too far in our AI rabbit hole right now, so I think we might want to just pause here, but thanks everyone for staying with us this long. We’re now going to just open this up for some questions and answers. So feel free to ask any of your questions, and we’re happy to respond to those in the chat
Jennifer: While everybody is putting their questions in the chat, there were two questions that I had for you guys while this was going on. First question I have, and you guys were talking about everybody’s trying to do less more with less, I was going to say less with more, and everybody would shake their heads at me.
Amber: Less with more. We had a lot of data, a lot of information, and it does make us not that productive, unfortunately. So that’s exactly.
Jennifer: But you know, when you’re trying to do more with less, when I get the phrasing right, how do hospitals get started on a digital aggregation strategy?
Amber: Yeah, I can take that one. Hospitals really, they need to start at their highest impact use cases. So just like we do QI, let’s find our problems first, and let’s start to think about what we need to accomplish. So whether it’s improving our quality metrics, we are needing to enable virtual care models because we have maybe less experienced nurses at the bedside. Side, or if it’s developing those predictive analytics. So from there, it’s just about building that scalable foundation that Erik was talking about. So, you need to be aggregating continuous, high-fidelity data across all of these various disparate sources, the different disparate units, and all the other systems, like the EHR, for example. And the key here is really just avoiding the siloed approach or projects, and instead just adopt a vendor-neutral platform that integrates seamlessly with what you already have, that’s existing infrastructure, which is going to allow you to scale over time without that heavy it. If you all can imagine, you’ve probably seen IT departments take the opposite approach, not taking vendor-neutral and saying, we need to rip and replace everything we have in the hospital and put in new things. Not only is that very costly, but it also takes a lot of time, when you consider all the amount of training and education that you need to actually accomplish integrating those systems into that future state.
Erik: And a lot of that is built around the physiologic monitor. They think they have to, they have to standardize the on the vendor, to standardize their strategy. And it’s really you need to look at all of the devices, and you need to have a standard approach to integrating and bringing in all of those technologies.
Jennifer: You know, with regard to that standard approach to integration, Eric, you said something earlier here about multidisciplinary team collaboration. With that in mind, when you’re you know, how do you involve your IT teams, your EHR teams, in the decision-making process of implementing a technology like Sick?
Erik: Yeah, I would say start early and often with that. A lot of times the initial approach is coming from the bedside. We need a tool. We need a better way to do something. So a lot of times, this is driven by that voice of the customer, the clinician, and the it and your EHR support teams are like, these are our internal customers. We need to support them, but bringing them to the table and say, This is what we want to accomplish. How can we give you the details so that you can build this in programmatically, budgetarily? So just like we’re having staffing shortages with clinicians, there are also staffing shortages and restraints within your IT departments and your EHR support teams and your analytic teams, so giving them as much runway as possible for planning this out, not only financially and from a budgetary standpoint of getting those integrations right, but also involving all of those members in the team to say these are the different important key steps that we’re going to need to take. So there’s a back and forth and an open dialogue among those teams. And I think hospitals are really beginning to bring all of those strategies together that the EMR, their clinical monitoring platforms, and their IT infrastructure, to support that really all have to work together. So, I would say, just like we would frequently say, involve your other clinicians early. It’s important to involve those other disciplinary teams early in this process to help develop your strategy.
Jennifer: I really appreciate that. And anybody else who’s had some questions here throughout the chat, we will be responding to those in the Q and A section and our comments. We’re going to leave that open for a little bit, but unfortunately, we’re out of time. So what I’d like to do, first of all is thank you too. This was a fantastic discussion with so many pieces of information that are relevant for everybody in healthcare today, on both sides of the fence, whether you’re an executive, clinical, your technical, as a marketer for the company, it’s interesting. You know, I come in with a background that’s not in healthcare, and I really appreciate that we’re doing this series, because I feel like I’m learning something every day on the job, and it’s something every day in the job that matters to me and my family and my loved ones, which is incredibly invaluable. So I really appreciate your guys expertise. And again, a reminder to everybody, we this is part of series. So this is number three of the Sickbay Sessions. We have our fourth Sickbay Session scheduled for July 17, so stay tuned on our LinkedIn and our website for information about that. This is going to be with our product team, and they’re going to be talking about delivering on commitments, implementing the future with Sickbay. So thank you, Amber, thank you Erik, for joining us today, and everybody. We look forward to seeing you for the next one you.
